Climatic niche comparison between closely related trans-Palearctic species of the genusOrthocephalus(Insecta: Heteroptera: Miridae: Orthotylinae)

Abstract

Previously climatic niche modelling had been studied for only a few trans-Palearctic species. It is unclear whether and to what extent those niches are different, and which climatic variables influence such a wide distribution. Here, environmental niche modelling is performed based on the Worldclim variables using Maxent for eight species of the genus Orthocephalus (Insecta: Heteroptera: Miridae: Orthotylinae). This group belongs to one of the largest insect families and it is distributed across Palearctic. Orthocephalus bivittatus, O. brevis, O. saltator and O. vittipennis are distributed across Europe and Asia; O. coriaceus, O. fulvipes, O. funestus, O. proserpinae have more limited distribution. Niche comparison using ENMTools was also undertaken to compare the niches of these species, and to test whether the niches of closely related species with trans-Palearctic distributions are more similar to each other, than to other congeners. It has been found that climatic niche models of all trans-Palearctic species under study are similar but are not identical to each other. This has been supported by niche geographic projections, climatic variables contributing to the models and variable ranges. Climatic niche models of all the trans-Palearctic Orthocephalus species are also very similar to two species having more restricted distribution (O. coriaceus, O. funestus). Results of this study suggest that trans-Palearctic distributions can have different geographic ranges and be shaped by different climatic factors.

Preferable and limiting conditions of trans-Palearctic <em>Orthocephalus</em> species (Heteroptera: Miridae)

“…Third, it was shown that the ecological niches of the trans-Palearctic species are similar to each other and to those of widespread species, which are distributed mostly in Europe (O. coriaceus) and Northeast Asia (O. funestus) (Namyatova, 2020). At the same time, the niches of the trans-Palearctic species are less similar to those of more local species inhabiting the Mediterranean areas of the western Palearctic (O. fulvipes and O. proserpinae).…”

“…These taxa must be able to adjust to different conditions, and they are very likely to be welladapted to the most common conditions in the regions, which include the largest biomes and climate zones. In the previous paper, based on the climatic data only, it was shown that four closely related trans-Palearctic Orthocephalus species are differently adapted to the climatic conditions, and their ecological niches are not identical (Namyatova, 2020). However, the species' preferences for the particular biomes and climate zones were not studied.…”

“…The 15 other species are either distributed very locally or known only from few localities (10 or less). Orthocephalus was revised morphologically and its distribution was carefully documented (see Namyatova andKonstantinov, 2009, andNamyatova, 2020, for further details on Ortho cephalus).…”

“…In recent years, different aspects of widely distributed species from various groups have been studied, including the peculiarities of native and introduced species, population structure, and climatic condition preferences, as well as the differences in tolerance of heat and cold between widespread species and those with comparatively small ranges (Lohman, Peggie, Pierce and Meier, 2008;Pyron and Burbrink, 2009;Overgaard, Kristensen, Mitchell and Hoffmann, 2011;Stohlgren et al, 2011;McDowell, Benson and Byers, 2014;Beukema et al, 2018;Namyatova, 2020). Widely distributed species can successfully complete their life cycle in a wide range of environmental conditions.…”

The critical role of tree species and human disturbance in determining the macrofungal diversity in Europe

“…The bias introduced by the difference between occurrence collection and background sampling can be avoided by ensuring that the background sample reflects the same bias as the presence data. Some studies show that biased background sampling can lead to much better predictive performance than random background sampling (Kramer‐Schadt et al, 2013; Namyatova, 2020; Phillips & Dudík, 2008; Phillips et al., 2009). This option of sampling biased background data is implemented in MaxEnt software by adding a bias grid, with which the cell values reflect sampling effort and give a weight to choose background data (Fourcade et al., 2014).…”